Triple
T6720430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vancouver 1954 |
E153378
|
entity |
| Predicate | multiSport |
P72873
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: true | Statement: [Vancouver 1954, multiSport, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: multiSport Context triple: [Vancouver 1954, multiSport, true]
-
A.
sportsAndRecreation
Indicates a relationship where an entity is associated with, involved in, or designated for sports or recreational activities.
-
B.
sportCategory
Indicates that one entity is classified as a type or category of sport to which the other entity (typically a specific sport or sporting event) belongs.
-
C.
relatedSport
Indicates that there is an association or connection between an entity and a particular sport.
-
D.
includesSport
Indicates that one entity contains, offers, or features a particular sport as part of its activities, content, or composition.
-
E.
sportNumber
Indicates the specific jersey or uniform number associated with an athlete in a sporting context.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c6880afb988190ad88011b48ecfcba |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d137084881908e04ee6b2bd45585 |
completed | March 27, 2026, 6:49 p.m. |
| PD | Predicate disambiguation | batch_69c6d08c5d348190a29dee668c398e70 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d11fab808190b18160ff3829fcc6 |
completed | March 27, 2026, 6:49 p.m. |
Created at: March 27, 2026, 2:07 p.m.